1. Field of the Invention
The present invention relates to a diagnostic imaging support processing apparatus and a diagnostic imaging support processing program product that support a diagnosis about an anatomic abnormality such as a nodular abnormality or a varicose vascular abnormality based on three-dimensional images collected by using a medical diagnostic imaging modality, e.g., an X-ray computer tomographic apparatus, an X-ray diagnostic apparatus, a magnetic resonance diagnostic apparatus, or an ultrasonic diagnostic apparatus.
2. Description of the Related Art
At the present day, a lung cancer heads a list of malignant deaths and goes on increasing in Japan. Therefore, a social demand for early detection is strong with respect to the lung cancer like precaution as a countermeasure for smoking. In each municipalities in Japan, a lung cancer examination based on a chest plain radiograph and a sputum cytodiagnosis is carried out. However, a report “Study Group Concerning Cancer Examination Effectiveness Evaluation” issued from Health and Welfare Ministry in Japan in 1998 concludes that a current lung cancer examination has effectiveness but it is small. An X-ray computer tomography (which will be referred to as a CT hereinafter) can readily detect a lung field type lung cancer as compared with a chest plain radiograph, but it was not able to be used for examination since its imaging time is long before 1990 when a helical scanning type CT (helical CT) appeared. However, soon after the helical CT appeared, a method of using a relatively low X-ray tube current to perform imaging for a reduction in radiation exposure (which will be referred to as a low-dose helical CT hereinafter) was developed, and a pilot study of a lung cancer examination using this method was carried out in Japan and the United States. As a result, a fact that the low-dose helical CT has a lung cancer detection rate greatly higher than that of the chest plain radiograph was proved.
On the other hand, a time required for imaging by the helical CT is kept being reduced due to an increasing number of CT detectors since 1998. The latest multi-detector helical CT can scan the entire lungs in less than 10 seconds with high spatial resolution that is nearly isotropic. Such a technological innovation has significantly increased the likelihood of small lung cancer being depicted by CT. However, high-resolution scanning with multi-detector helical CT has the drawback of considerably increasing the workload of image interpretation since hundreds of images are generated per scan.
Because of this situation, it is widely recognized that a computer assisted diagnosis (which will be referred to as a CAD hereinafter) using a computer to avoid overlooking lung cancer is required for the low-dose helical CT to be established as a lung cancer examination method.
Since a small lung cancer in a lung field appears as a nodular abnormality in a CT image, automatic detection of such an abnormality is an important theme, and various studies have been conducted since the 1990's (see, e.g., “David S. Paik and seven others, “Surface Normal Overlap: A Computer-aided Detection Algorithm with Application to Colonic Polyps and Lung Nodules in Helical CT”, IEEE Transactions on Medical Imaging, Vol. 23, No. 6, June 2004, pp. 661-675”).
The present applicant has suggested a technology for analyzing a nodule candidate and a peripheral structure thereof to automatically three-dimensionally detect a nodule as JP-A 2006-239005 (KOKAI) (WO/2006/093085). The present applicant has also suggested a technology related to display of an automatically detected nodular region and a peripheral region thereof in JP-A 2008-12291 (KOKAI). The present applicant has further suggested a technology for analyzing a nodule and a peripheral region thereof to automatically determine an anatomic malignancy of the nodule in JP-A 2008-7033 (KOKAI).
Meanwhile, in the lungs that are major target organs for CAD, the following physiological/pathological changes on CT are frequently observed in contact with the pleura.
(1) A slight increase in lung field density (a ground-glass opacity) reflecting venous stasis or reduced ventilation is often seen underneath the pleura, and when this increase in density is not uniform, the such areas with nonuniform density can appear as vague nodules.
(2) Findings of fibrosis or scars presumably resulting from previous pleuritis and/or pneumonia are very common along the pleura.
(3) Preclinical interstitial pulmonary diseases can be incidentally revealed by CT. In such cases, it is not uncommon for alveolitis due to these diseases, which tends to occur near the pleura, to appear as local ground-glass opacity.
For physicians who undertake diagnostic imaging, it is a simple task to discriminate the above-described abnormalities near the pleura from possibly malignant nodules. In most cases, this judgment is made by viewing axial images only, which implies that two-dimensional morphological information, i.e., mainly silhouette or shape, of lesions is helpful enough in this judgment.
Meanwhile, in the diagnosis of small lung lesions, polygonal shape is reported as one of the criteria predictive for benign lesions (see, e.g., Takashima S., et al. Small solitary pulmonary nodules (1 cm) detected at population-based CT screening for lung cancer: Reliable high-resolution CT features of benign lesions. AJR Am J Roentgenol. 2003; 180: 955-964).
For example, when the contour of a small pulmonary lesion can be well approximated by a triangle, the lesion is most likely a scar and thus can be safely disregarded. In general, for a lesion whose contour is well approximated by a polygon with n vertices, the possibility that the lesion is neoplastic can be considered to increase as n increases, because the shape of the lesion becomes closer to a circle as n increases. Thus a quantity that monotonically increases with n is desired in order to represent the shape characteristics of small lung lesions. Classically, one such quantity is the degree of circularity defined as 4πA/L2 where A and L are the area and circumference of a two-dimensional object, respectively. For given L, A is maximized when the object is perfectly circular. Thus the maximum value of the degree of circularity is 1.
In CAD, the degree of circularity may be used as one of the parameters for the selection among intermediate nodule candidates. In this case, by using the threshold for the degree of circularity set to e.g., 0.8, nodule candidates with the values of the degree of circularity not less than the threshold are selected.
Suppose that there is a semi-circular shaped nodule in contact with the pleura. Intuitively, such a nodule is round enough to be described as protruding, implying that the nodule is possibly neoplastic. For this nodule, the value of the degree of circularity defined above is approximately 0.75 because the shape of the nodule is semicircular. Therefore, when the threshold for the degree of circularity is 0.8, this nodule is not selected, thus resulting in false-negative detection which decreases the sensitivity of CAD. Such false-negative detection can be avoided by lowering the threshold to e.g., 0.6. By so doing, however, the number of false positives most likely increases.
As indicated by the foregoing, for lesions in contact with the pleura, the above degree of circularity does not well quantify the intuitive roundness of the lesion. Hence this degree of circularity is considered to be unsuitable as a parameter for the selection of nodule candidates in CAD of pulmonary nodules.